






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__UpperCamelCase   :   Optional[int]      =    logging.get_logger(__name__)


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Dict =							['pixel_values']







	def __init__(	self   :				int							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				Union[int, float] = 1 / 255							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				int = 8							,					**_lowerCAmelCase   :				str							,					)		->			None:
		"""simple docstring"""







		super().__init__(**_lowerCAmelCase      )

		__lowercase   							=	do_rescale
		__lowercase   							=	rescale_factor
		__lowercase   							=	do_pad
		__lowercase   							=	pad_size







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				np.ndarray							,					_lowerCAmelCase   :				float							,					_lowerCAmelCase   :				Optional[Union[str, ChannelDimension]] = None							,					**_lowerCAmelCase   :				Any      )		->			np.ndarray:
		"""simple docstring"""







		return rescale(_lowerCAmelCase							,					scale=_lowerCAmelCase							,					data_format=_lowerCAmelCase							,					**_lowerCAmelCase      )







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				np.ndarray							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Optional[Union[str, ChannelDimension]] = None      )		->			str:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	get_image_size(_lowerCAmelCase      )
		__lowercase   							=	(old_height // size + 1) * size - old_height
		__lowercase   							=	(old_width // size + 1) * size - old_width

		return pad(_lowerCAmelCase							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=_lowerCAmelCase      )







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				ImageInput							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				Optional[float] = None							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				Optional[int] = None							,					_lowerCAmelCase   :				Optional[Union[str, TensorType]] = None							,					_lowerCAmelCase   :				Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**_lowerCAmelCase   :				Any							,					)		->			Any:
		"""simple docstring"""







		__lowercase   							=	do_rescale if do_rescale is not None else self.do_rescale
		__lowercase   							=	rescale_factor if rescale_factor is not None else self.rescale_factor
		__lowercase   							=	do_pad if do_pad is not None else self.do_pad
		__lowercase   							=	pad_size if pad_size is not None else self.pad_size

		__lowercase   							=	make_list_of_images(_lowerCAmelCase      )

		if not valid_images(_lowerCAmelCase      ):
			raise ValueError(
			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
			    """torch.Tensor, tf.Tensor or jax.ndarray."""      )

		if do_rescale and rescale_factor is None:
			raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

		# All transformations expect numpy arrays.
		__lowercase   							=	[to_numpy_array(_lowerCAmelCase      ) for image in images]

		if do_rescale:
			__lowercase   							=	[self.rescale(image=_lowerCAmelCase							,					scale=_lowerCAmelCase      ) for image in images]

		if do_pad:
			__lowercase   							=	[self.pad(_lowerCAmelCase							,					size=_lowerCAmelCase      ) for image in images]

		__lowercase   							=	[to_channel_dimension_format(_lowerCAmelCase							,					_lowerCAmelCase      ) for image in images]

		__lowercase   							=	{"""pixel_values""": images}
		return BatchFeature(data=_lowerCAmelCase							,					tensor_type=_lowerCAmelCase      )


"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
